基于胶囊网络的智能交通标志识别方法

Intelligent traffic sign recognition method based on capsule network

针对卷积神经网络的标量神经元无法表达特征位置信息,对复杂的车辆行驶环境适应性差,导致交通标志识别率低的问题,提出一种基于胶囊网络的智能交通标志识别方法。首先采用超深度卷积神经网络改进特征提取部分,然后在主胶囊层引入池化层,最后采用移动指数平均法改进了动态路由算法。在GTSRB数据集上的测试结果表明,改进后的胶囊网络方法在特殊场景下的识别精度提高了10.02个百分点,相对于传统的卷积神经网络,该方法的单张图片的识别时间缩短了2.09 ms。实验结果表明,改进后的胶囊网络方法能满足准确、实时的交通标志识别要求。

The scalar neurons of convolutional neural networks cannot express the feature location information,and have poor adaptability to the complex vehicle driving environment,resulting in low traffic sign recognition rate.Therefore,an intelligent traffic sign recognition method based on capsule network was proposed.Firstly,the very deep convolutional neural network was used to improve the feature extraction part.Then,a pooling layer was introduced in the main capsule layer.Finally,the movement index average method was used for improving the dynamic routing algorithm.The test results on the GTSRB dataset show that the improved capsule network method improves the recognition accuracy in special scenes by 10.02 percentage points.Compared with the traditional convolutional neural network,the proposed method has the recognition time for single image decreased by 2.09 ms.Experimental results show that the improved capsule network method can meet the requirement of accurate and real-time traffic sign recognition.